Francesco Banterle

Inverse tone mapping

In recent years many Tone Mapping Operators (TMOs) have been presented in order to display High Dynamic Range Images (HDRI) on typical display devices. TMOs compress the luminance range while trying to maintain contrast. The dual of tone mapping, inverse tone mapping, expands a Low Dynamic Range Image (LDRI) into a HDRI. HDRIs contain a broader range of physical values that can be perceived by the human visual system. The majority of todays media is stored in low dynamic range. Inverse Tone Mapping Operators (iTMOs) could thus potentially revive all of this content for use in high dynamic range display and image-based lighting. We propose an approximate solution to this problem that uses median-cut to find the areas considered of high luminance and subsequently apply a density estimation to generate an Expand-map in order to extend the range in the high luminance areas using an inverse Photographic Tone Reproduction operator.

A Survey of Specularity Removal Methods

The separation of reflection components is an important issue in computer graphics, computer vision and image processing. It provides useful information for the applications that need consistent object surface appearance, such as stereo reconstruction, visual recognition, tracking, objects re‐illumination and dichromatic editing. In this paper we will present a brief survey of recent advances in separation of reflection components, also known as specularity (highlights) removal. Several techniques that try to tackle the problem from different points of view have been proposed so far. In this survey, we will overview these methods and we will present a critical analysis of their benefits and drawbacks.

High Dynamic Range Imaging and Low Dynamic Range Expansion for Generating HDR Content

In the last few years, researchers in the field of High Dynamic Range (HDR) Imaging have focused on providing tools for expanding Low Dynamic Range (LDR) content for the generation of HDR images due to the growing popularity of HDR in applications, such as photography and rendering via Image‐Based Lighting, and the imminent arrival of HDR displays to the consumer market. LDR content expansion is required due to the lack of fast and reliable consumer level HDR capture for still images and videos. Furthermore, LDR content expansion, will allow the re‐use of legacy LDR stills, videos and LDR applications created, over the last century and more, to be widely available. The use of certain LDR expansion methods, those that are based on the inversion of Tone Mapping Operators (TMOs), has made it possible to create novel compression algorithms that tackle the problem of the size of HDR content storage, which remains one of the major obstacles to be overcome for the adoption of HDR. These methods are used in conjunction with traditional LDR compression methods and can evolve accordingly. The goal of this report is to provide a comprehensive overview on HDR Imaging, and an in depth review on these emerging topics.

Expanding low dynamic range videos for high dynamic range applications

In this paper we introduce an algorithm and related methods that expand the contrast range of Low Dynamic Range (LDR) videos in order to regenerate missing High Dynamic Range (HDR) data. For content generated from single exposure LDR sequences, this is clearly an under constrained problem. We achieved the expansion by inverting established tone mapping operator, a process we term inverse tone mapping. This approach is augmented by a number of methods which help expand the luminance for the required pixels while avoiding artifacts. These methods may be used to convert the large libraries of available legacy LDR content for use, for instance, on new content-starved HDR devices. Moreover, these same methods may be used to provide animated emissive surfaces for image based lighting (IBL). We demonstrate results for all the above applications and validate the resultant HDR videos with original HDR references using the HDR Visual Difference Predictor (HDR-VDP) image metric.

A framework for inverse tone mapping

In recent years many tone mapping operators (TMOs) have been presented in order to display high dynamic range images (HDRI) on typical display devices. TMOs compress the luminance range while trying to maintain contrast. The inverse of tone mapping, inverse tone mapping, expands a low dynamic range image (LDRI) into an HDRI. HDRIs contain a broader range of physical values that can be perceived by the human visual system. We propose a new framework that approximates a solution to this problem. Our framework uses importance sampling of light sources to find the areas considered to be of high luminance and subsequently applies density estimation to generate an expand map in order to extend the range in the high luminance areas using an inverse tone mapping operator. The majority of today’s media is stored in the low dynamic range. Inverse tone mapping operators (iTMOs) could thus potentially revive all of this content for use in high dynamic range display and image based lighting (IBL). Moreover, we show another application that benefits quick capture of HDRIs for use in IBL.

Multidimensional image retargeting

Retargeting refers to the process by which an image or video is adapted from the display device for which it was meant (target display) to another one (retarget display). The retarget display has different features from the target one such as dynamic range, discretization levels, color gamut, multi-view, and refresh rate spatial resolution. This is a very relevant topic in graphics, given the increasing number of display devices from large, high-contrast screens to small cell phones with limited dynamic range; a lot of techniques are being published in different venues, and its hard to keep up. For most cases retargeting can be an ill-posed problem, for example in the process of displaying Low Dynamic Range (LDR) or 8-bit content on High Dynamic Range (HDR) displays. Such a problem requires the retargeting algorithm to generate new content which is missing in the input image/frame. In this course, we will present the latest solutions and techniques for retargeting images along various dimensions such as dynamic range, colors, temporal and spatial resolutions, and for the first time offer a much-needed holistic view of the field. Moreover, we are going to show how to measure and analyze the changes applied to an image or video in terms of quality using both psychophysical experiments (subjective) and computational metrics (objective). The course should be of interest to anyone involved in graphics in a broader sense, given the almost unavoidable need to retarget results to different devices -from developers interested in implementing retargeting techniques, to users that just need an overall perspective. For researchers fully engaged in developing multi-dimensional retargeting techniques, this course will serve as a solid background for future algorithms.

A Low-Memory, Straightforward and Fast Bilateral Filter Through Subsampling in Spatial Domain

In this work we present a new algorithm for accelerating the colour bilateral filter based on a subsampling strategy working in the spatial domain. The base idea is to use a suitable subset of samples of the entire kernel in order to obtain a good estimation of the exact filter values. The main advantages of the proposed approach are that it has an excellent trade‐off between visual quality and speed‐up, a very low memory overhead is required and it is straightforward to implement on the GPU allowing real‐time filtering. We show different applications of the proposed filter, in particular efficient cross‐bilateral filtering, real‐time edge‐aware image editing and fast video denoising. We compare our method against the state of the art in terms of image quality, time performance and memory usage.

A psychophysical evaluation of inverse tone mapping techniques

In recent years inverse tone mapping techniques have been proposed for enhancing low‐dynamic range (LDR) content for a high‐dynamic range (HDR) experience on HDR displays, and for image based lighting. In this paper, we present a psychophysical study to evaluate the performance of inverse (reverse) tone mapping algorithms. Some of these techniques are computationally expensive because they need to resolve quantization problems that can occur when expanding an LDR image. Even if they can be implemented efficiently on hardware, the computational cost can still be high. An alternative is to utilize less complex operators; although these may suffer in terms of accuracy. Our study investigates, firstly, if a high level of complexity is needed for inverse tone mapping and, secondly, if a correlation exists between image content and quality. Two main applications have been considered: visualization on an HDR monitor and image‐based lighting.

High-dynamic-range video solution

The natural world presents our visual system with a wide, ever-changing range of colors and intensities. Existing video cameras are only capable of capturing a limited part of this wide range with sufficient resolution. High-dynamic-range (HDR) images can represent most of the real worlds luminances, but until now capturing HDR images with a linear-response function has been limited to static scenes. This demonstration showcases a novel complete HDR video solution. The system includes a unique HDR video camera capable of capturing a full HDTV video stream consisting of 20 f-stops dynamic range at a resolution of 1920 x 1080 pixels at 30 frames per second; an encoding method for coping with the huge amount of data generated by the camera (achieving a compression ratio of up to 100:1 and real-time decompression); and a new 22-inch desktop HDR display for directly visualizing the dynamic HDR content.

Perceptual rendering of participating media

High-fidelity image synthesis is the process of computing images that are perceptually indistinguishable from the real world they are attempting to portray. Such a level of fidelity requires that the physical processes of materials and the behavior of light are accurately simulated. Most computer graphics algorithms assume that light passes freely between surfaces within an environment. However, in many applications, we also need to take into account how the light interacts with media, such as dust, smoke, fog, etc., between the surfaces. The computational requirements for calculating the interaction of light with such participating media are substantial. This process can take many hours and rendering effort is often spent on computing parts of the scene that may not be perceived by the viewer. In this paper, we present a novel perceptual strategy for physically based rendering of participating media. By using a combination of a saliency map with our new extinction map (X map), we can significantly reduce rendering times for inhomogeneous media. The visual quality of the resulting images is validated using two objective difference metrics and a subjective psychophysical experiment. Although the average pixel errors of these metric are all less than 1%, the subjective validation indicates that the degradation in quality still is noticeable for certain scenes. We thus introduce and validate a novel light map (L map) that accounts for salient features caused by multiple light scattering around light sources.